Exploring nonlinear feature space dimension reduction and data representation in breast CADx with Laplacian eigenmaps and ‐SNE
Abstract
In this preliminary study, recently developed unsupervised nonlinear dimension reduction (DR) and data representation techniques were applied to computer‐extracted breast lesion feature spaces across three separate imaging modalities: Ultrasound (U.S.) with 1126 cases, dynamic contrast enhanced magnetic resonance imaging with 356 cases, and full‐field digital mammography with 245 cases. Two methods for nonlinear DR were explored: Laplacian eigenmaps [M. Belkin and P. Niyogi, “Laplacian eigenmaps for dimensionality reduction and data representation,” Neural Comput. 15, 1373–1396 (2003)] and ‐distributed stochastic neighbor embedding (‐SNE) [L. van der Maaten and G. Hinton, “Visualizing data using t‐SNE,” J. Mach. Learn. Res. 9, 2579–2605 (2008)].
Document Details
- Document Type
- Pub Defense Publication
- Publication Date
- Dec 22, 2009
- Source ID
- 10.1118/1.3267037
Entities
People
- Andrew R. Jamieson
- Hui Li
- Karen Drukker
- Maryellen Lissak Giger
- Neha Bhooshan
- Yading Yuan
Organizations
- National Institutes of Health
- United States Department of Defense
- United States Department of Energy